tbuckley/cabot-search huggingface dataset:
Validating the use of machine-learning cancer staging algorithms for Medicare cost analyses.
Authors: Rebecca Smith, Lesley-Ann Miller-Wilson, Gebra Cuyun Carter, Ifrah Fayyaz, Andreyah Pope, Bruce Pyenson
Journal: Journal of Clinical Oncology (2023)

e13548 Background: Administrative claims provide valuable real-world insight into the care of cancer patients; however, claims data lacks cancer stage information. This limitation constrains research on the value of early diagnosis and treatment as well as on the costs and savings associated with increased cancer screening. In prior work, our team used the SEER-Medicare data to develop machine learning (ML) algorithms to stage non-small cell lung (NSCLC), colon (CC), and rectal (RC) cancer patients using clinical flags derived from claims data. These algorithms were 69% (RC), 78% (NSCLC), and 83% (CC) accurate at matching incident cancer patients with their SEER-recorded AJCC stage (SEER-stage) at diagnosis. This work sought to test whether these ML algorithms are sufficiently accurate for use in claim cost analyses. Methods: Incident NSCLC, CC, and RC patients were identified using 2016-2017 SEER-Medicare data and assigned a cancer stage using a claims-based predictive multinomial logistic regression model (R Statistical Software - v4.1.2 R Core Team 2021; nnet package - Venables and Ripley 2002). Patients’ cumulative medical and pharmacy costs were summarized for 12 months starting with patients’ index month. Patients’ Medicare index month was set equal to the month of their first claim with a cancer diagnosis. Patients’ SEER index month was set equal to the diagnosis month associated with their incident tumor record in SEER. Patients with each cancer type were then grouped two ways - by ML-stage and by SEER-stage. Median patient costs were compared between stage groups for each cancer type and differences tested for statistical significance using Wilcoxon Rank-Sum Testing. Results: For NSCLC and CC, raw differences in median 12-month claim costs between the ML- and SEER-stage cohorts were small (1%-4%). Cost differences for RC were higher (7%-17%). ML and SEER costs were not significantly different (p &gt; 0.05) between later-stage cohorts (NSCLC stages 3 and 4, CC stages 2C/3 and 4, and RC stage 4); however, early-stage groups were always significantly different (p &lt; 0.05). Conclusions: Although costs were not statistically equivalent across all stage groups, the similarity of ML and SEER costs across higher-stage cohorts and small raw differences in median costs for each NSCLC and CC group suggests that ML algorithms with higher accuracy may be used to develop costs from administrative data for stage shift modeling and cost tradeoff analyses. [Table: see text]
Temporal trends of atypical drug access at an academic oncology unit: The Verona University Hospital experience.
Authors: Francesca Mansa Atsina, Alice Rossi, Ilaria Bolcato, Roberta Fraccaroli, Chiara Alberti, Laura Scardoni, Isabella Patruno, Francesca Zacchi, Viola Moscarda, Luisa Messineo, Eleonora Lucin, Ilaria Mariangela Scaglione, Alessandra Dodi, Michele Pavarana, Davide Melisi, Roberto Leone, Giovanna Scroccaro, Paola Marini, Michele Milella
Journal: Journal of Clinical Oncology (2023)

e13651 Background: In Italy, patient (pt) access to oncological drugs is almost exclusively provided by the NHS, following authorization by the European Medicines Agency (EMA) and negotiation with the Italian Medicines Agency (AIFA) for specific indications. However, regulatory delays (with respect to the availability of scientific evidence) and the need for rapid access to innovative drugs, particularly in the context of orphan and rare diseases, have prompted the utilization of atypical drug access pathways. In addition to enrollment in clinical trials, atypical drug access pathways are expected to be increasingly used in the context of a Precision Oncology practice. Methods: We explored trends in atypical drug access at the Oncology Section of the Verona University and Hospital Trust, by interrogating Oncology and Pharmacy databases and regional registries of rare diseases and reviewing AIFA registries records. Data on atypical drug access were also correlated with the use of NGS-based molecular profiling. Results: Between January 2016 and December 2022, a total of 355 atypical drug access requests for the treatment of cancer pts were recorded, with a peak in 2017 and no discernible temporal trend. Over this period, the two most common atypical access schemes were compassionate (including named patient and early access programs, 61%) and off-label (21%) use. The latter steadily increased over time (from 8% in 2017 to 39% in 2021). Overall, the type of drugs requested through atypical access were almost equally distributed between classical cytotoxic agents (CHT, 36%), immuno-oncology drugs (IO, 29%) and molecularly targeted agents (TT, 35%). Temporal trends in the usage of different classes of drugs are shown. Interestingly, TT requests decreased between 2016 and 2018 and stabilized at approximately 50% of total requests since 2019. Between 2019 and 2021, atypical drug access requests triggered by NGS-based extended molecular profiling significantly increased from 2.8% to 23.1% (p=0.0007). Conclusions: Although atypical drug access did not increase overall during the observation period, a trend towards increased off-label use of TT was identified, especially from 2019 onwards; figures for 2022 are being analyzed and will be presented at the Meeting. Since 2019, NGS-informed requests have significantly increased to constitute almost 1/4 of all atypical drug access requests, clearly identifying a trend towards implementation of Precision Oncology in routine clinical practice and evolving towards the implementation of Molecular Tumor Board case discussions. [Table: see text]
Trends in new and persistent opioid use in older adults with cancer.
Authors: Laura Van Metre Baum, Madhav KC, Pamela R. Soulos, Molly Jeffery, Kathryn Jean Ruddy, Michael Leapman, Vikram Jairam, Michaela Ann Dinan, Catherine Lerro, Corinne Woods, Hana Lee, Amy Ho, David J. Graham, Mark Liberatore, Donna Rivera, Cary Philip Gross, Henry Soo-Min Park
Journal: Journal of Clinical Oncology (2023)

1592 Background: While ongoing efforts to decrease opioid use have dramatically altered the prescribing landscape, the impact on patients with cancer is not well understood. We examined trends in new and persistent opioid use in opioid-naïve older patients with and without cancer in the United States. Methods: This retrospective cohort study used SEER-Medicare linked data for patients with solid tumors malignancies diagnosed from 2012 through 2017, and a random sample of patients without cancer. We included patients who did not receive opioids for 3 months prior to the diagnosis or randomly assigned index date. We identified new opioid use (claim in 0-90 days after diagnosis/index date), early persistent opioid use (claims in days 0-90 and 91-180), and long-term persistent opioid use (claims in days 0-90 and 91-180 and months 13-15). Further analysis focused on 4 clinical strata: non-cancer, metastatic cancer, cancer treated with surgery alone, and cancer treated with surgery plus chemotherapy and/or radiotherapy ("surgery+"). We calculated relative changes in the predicted probability of opioid use from 2012 to 2017 based on a logit model adjusted for patient demographics. Results: We identified 238,470 eligible patients with cancer (mean age 76.3 years). Among patients with cancer, 46.4% had new opioid use; use was higher among those receiving surgery+ (70.6%), those treated with surgery alone (58.8%) and those with metastatic cancer (50.0%), compared to patients without cancer (6.9%). From 2012 to 2017, the predicted probability of new opioid use declined in all groups, with lower relative decline in the cancer subgroups (6.8% surgery+; 9.5% surgery alone; 10.2% metastatic cancer) compared to the non-cancer comparator (24.7%). For instance, in patients with metastatic cancer, predicted probability of new use decreased from 52.4 to 47.1% (10.2%) vs. 8.0 to 6.0% (24.7%) in patients without cancer. Relative decline in predicted probability of long-term persistent opioid use was lower in the surgical cancer strata (44.2% surgery+; 46.1% surgery alone) vs. the non-cancer comparator (54.0%). Conclusions: While opioid use remains common in patients with cancer, new and persistent opioid use declined over time in all patients, though with steeper relative declines in non-cancer compared to cancer populations. Our study suggests important but tempered effects of the changing opioid climate on patients with cancer.[Table: see text]
Prognostic value of HER2-low status in breast cancer: A systematic review and meta-analysis.
Authors: Chiara Molinelli, Flavia Jacobs, Elisa Agostinetto, Guilherme Nader Marta, Marcello Ceppi, Marco Bruzzone, Eva Blondeaux, Francesco Schettini, Aleix Prat, Giuseppe Viale, Lucia Del Mastro, Matteo Lambertini, Evandro de Azambuja
Journal: Journal of Clinical Oncology (2023)

1104 Background: HER2-Low breast cancer (BC) has been recently identified as a new therapeutic target. However, it is unclear if HER2-Low status has an independent impact on prognosis, both in metastatic and early settings. Methods: A systematic literature research of PubMed, Cochrane and conference proceedings (ASCO meetings, SABCS and ESMO congress) up to December 8, 2022 was performed to identify studies comparing survival outcomes of patients affected by HER2-Low BC vs. HER2-zero BC. HER2-Low status was defined as immunohistochemistry score 1 + or 2+ without in situ hybridization amplification. Pooled hazard ratios (HRs) for survival endpoints in the metastatic [progression-free survival (PFS), overall survival (OS)] and in the early [disease free-survival (DFS) and OS] settings as well as their 95% confidence intervals (95%CI) were calculated using the random-effect model of Der Simonian and Laird. The above-mentioned endpoints were also assessed according to estrogen receptor (ER) status. Results: Among 1,916 identified records, 42 studies including 1,797,377 patients were eligible for this analysis. We show the pooled HRs in the overall population and specific subgroups. In the early setting, HER2-Low status was associated with significant improved DFS (HR 0.86, 95%CI 0.79-0.92, p&lt;0.001) and OS (HR 0.84, 95%CI 0.77-0.92, p&lt;0.001) when compared to HER2-zero status. Improved OS was observed in subgroup analysis for both ER-positive and ER-negative HER2-Low populations, while DFS improvement was observed only in the ER-positive subgroup. Similarly, in the metastatic setting, patients affected by HER2-Low BC showed better OS when compared to those with HER2-zero BC (HR 0.94, 95%CI 0.89-0.98, p=0.008). HER2-Low metastatic BC was associated with significant improvement in OS compared to HER2-zero BC also in ER-positive and ER-negative subgroups. No significant differences were found in terms of PFS. Conclusions: Compared with HER2-zero status, HER2-Low status appears to be associated with an increased OS both in the advanced and early settings, regardless of hormone-receptor expression. [Table: see text]
Are we using the right cut-offs to obtain maximum information from Ki-67 in patients with early breast cancer?
Authors: Oana Gabriela Trifanescu, Liviu Bilteanu, Silvia Ilie, Diana Maria Bran, Inga Safta, Laurentia Minea Gales, Rodica Maricela Anghel
Journal: Journal of Clinical Oncology (2023)

e12567 Background: Ki-67 is one of the most critical prognostic proliferation markers in breast cancer, used on a large scale by oncologists to select treatment options. This study aimed to validate Ki-67 as a prognostic marker and establish the optimal Ki67 cut-offs for stratifying patient prognosis and treatment decisions. Methods: Medical files of 582 consecutive patients with early breast cancer (stage IB-IIIA) who underwent surgery as a first treatment in our institution between 2005-2019 were retrospectively reviewed. Values of Ki-67 were divided into 5 categories: 0-9%, 10-19%, 20-29%, 30-39%, and more than 40%. Results: Kolmogorov-Smirnov and Shapiro-Wilk tests showed that the patient’s age (yrs) at diagnostic, Ki-67 (in %), tumor size (in mm), estrogen and progesterone receptors abundance (%), exhibit normal distributions (p &lt; 0.05). Median age at diagnosis was 54.8 (± 11.5) years and mean Ki-67 of 25.5 ± 16.5 and a median value of 20. Stage distribution was: stage IB 5.5%, IC 12%, IIA 46.6%, IIB 20.4%, IIIA 15.5%. The mean tumor dimension at diagnosis was 21.3 mm. After a median follow-up of 48 months (6-118), estimated disease-free survival rates (DFS) for all patients at 3 and 5 years were 92% and 79%. Disease-free survival rates at 5 years was 96% for patients with Ki-67 0-9%, 90% for patients with Ki-67 10-19%, 80% for patients with Ki-67 20-29%, 75% for patients with Ki-67 30-39%, and 72% for patients with Ki-67 more than 40%. The difference between the median DFS and each ki67 category was statistically significant (p &lt; 0.0001). Using the fit of mixture method, we identify a new cut-off of 29% that negatively predicts DSF for patients (HR = 4.51 95% CI = 1.69-12.04, p = 0.001). Pearson correlation coefficients (p &lt; 0.05) for Ki-67 and DFS, age, ER%, PR%, p53, and tumor size were -0.110, -0.096, -0.139, -0.136, 0.301, and 0.175, respectively. Moreover, DFS was also negatively correlated (p &lt; 0.01) with the patient’s age (-0.106) and tumor size (-0.180). Though the correlation coefficients were small, they oriented the creation of linear models of DFS as a dependent variable and Ki-67, tumor size, and age. DFS has a statistically significant dependence only on the first two variables (standardized coefficients -0.103, p = 0.045 and -0.161, p = 0.02). Conclusions: Ki-67 is a strong prognostic factor in early breast cancer patients. New Ki-67 cut-off levels that may be used in clinical practice were identified. In addition, multivariate analysis showed a significant statistical influence of other factors, such as Ki67 and tumor size. Such correlations can be the basis of a scoring system for progression risk.
Evidence supporting innovative cancer care delivery model for overcoming health disparity.
Authors: Dinesh Pendharkar, Chandramauli Tripathi, Kalpana Srivastav, Uttam Kumar, Ashok Kumar Nagar
Journal: Journal of Clinical Oncology (2023)

e13521 Background: Disparity in cancer care is the biggest challenge faced by the health systems. The solution could be brought out by new models of service delivery. To mitigate the challenge, an innovative district cancer care model was launched across 6 states of India. A cancer care unit has been established in government district hospitals, which constitute pivot of health systems in all low- and middle-income countries of the world . It assists cancer patients in complete care continuum from diagnosis to end of life care. Here, we report how a district cancer care model (Pendharkar model) proved its validity during the covid pandemic, by providing relentless cancer care, when specialized cancer services were paralyzed in the country. Methods: Patient data were collected from four government district hospitals of Ujjain (Madhya Pradesh) Bhilwara, Jhalawar and Churu (Rajasthan), over three years, including period of Covid pandemic and lockdown. The numbers were analyzed for attendance in outpatient and inpatient services, numbers of chemotherapy sessions administered, from January 2019 to December 2021. Standard statistical calculations were done. Results: A total of 4873 new patients were registered in the cancer units (1614,1678,1581 per year), over the period of three years: 2019, 2020, 2021. Annually, there were 695,643 and 609, new patients registered in the Ujjain district, 190,252 and 204 in Churu, 349. 276.and 276 in Jhalawar, and 380,507 and 442 in Bhilwara. Over a three-year period, there were 37183 outpatient visits in total (11416, 13665, and 12102 in 2019, 2020, and 2021 respectively). The number of inpatient admissions were 18593 (5047,7077,6469 respectively). A total of 17639 sessions of chemotherapy were performed during this period (5133,6361,8745 in 2019, 2020, and 2021 respectively). The data showed no change despite pandemic lockdown. Conclusions: Utilization of outpatient and inpatient services, as well procedures rendered, did not change despite covid and lockdown in caner units of government district hospitals. The data clearly demonstrates the robustness and resilience of this health care model of system strengthening, using alternate oncology workforce, task sharing and capacity building. It worked equally efficiently in different parts of India and maintained its effectiveness even during pandemic, when complete collapse of healthcare was noticed around the world. The oncology societies and international organizations could take more interest in analyzing and improving this delivery model, which has immense capability of reducing health disparities.[Table: see text]
Reliance of agency-approved and guideline-recommended therapies on surrogate endpoints in triple-negative breast cancer (TNBC).
Authors: Kinisha Gala, Ankit Kalucha, Judith Pérez-Granado, Maria Garcia Requesens, Laura Vidal, Bruno Larvol, Mark Gramling, Kamal S. Saini
Journal: Journal of Clinical Oncology (2023)

e13085 Background: Surrogate endpoints, such as progression-free survival (PFS) and event-free survival (EFS), are used for accelerated approval in various cancers, including TNBC. Here we review the use of surrogates and clinical outcomes in determining agency approval and guideline recommendation. Methods: Phase 2/3 TNBC trials and trials including TNBC subgroups reporting hazard ratios (HR) for clinical and surrogate endpoints were identified through a systematic search of LARVOL CLIN, PubMed, and conference proceedings. Trials that only evaluated cytotoxic agents and did not report EFS, PFS and/ or overall survival (OS) HR values were excluded. We performed a descriptive analysis of agency and guideline approvals in relation to therapeutic benefit as measured by surrogate outcomes and OS. Results: 21 TNBC cohorts and 1 pooled analysis studying 17 different targeted therapies for TNBC were included. There are 3 targeted therapies that have agency approval and 7 targeted therapies that are recommended by either the NCCN, EMA and ASCO guidelines. Of the two FDA and EMA-approved therapies, pembrolizumab + chemotherapy (PBL + CHT) showed PFS/EFS benefit for early TNBC and PDL1+ metastatic (m) TNBC but only OS benefit for PDL1+ mTNBC, whereas sacituzumab showed significant benefit for both PFS and OS for mTNBC. Four therapies (olaparib for BRCA+ mTNBC, bevacizumab for early TNBC, talazoparib for BRCA+ mTNBC and PBL + CHT for PD-L1+ mTNBC) are recommended by guidelines without having a significant OS benefit. Conclusions: Surrogate outcomes are heavily relied upon for TNBC guideline recommendation. Despite the need for timely access to novel TNBC therapeutics, clinical outcomes should be followed to ensure benefit once they have been reached. An up-to-date digitized trial results repository would ensure expedient review of clinical endpoints. [Table: see text]
Best practices study to enhance the quality of multi-disciplinary teams in lung cancer care.
Authors: Marcos Gallego Llorente, Lidewey Verbaas, Yingjing Poka Cui, Marcio Gomes, Adrien Moucquot, Heather Moses, David Dellamonica, Bas Amesz
Journal: Journal of Clinical Oncology (2023)

1532 Background: Regular meetings at multi-disciplinary teams (MDTs) constitute a key moment in the care pathway in lung cancer, where physicians collectively discuss patient cases and decide on treatment plans. MDTs arguably increase the volume of patients treated, improve diagnosis, and positively impact on survival. However, the way MDTs are organized and function is variable across hospitals, countries and continents. Variations in MDT processes may lead to sub-optimal outcomes, such as inconsistencies on the numbers of patients treated with curative intent as well as variable 5-years survival rates for lung cancer. The importance of MDTs is ever increasing, as the therapeutical landscape is getting more complex every year, and treatments are evolving from a one-size-fits-all to a personalized approach. Methods: In this study, we gather the best practices in MDT processes, capabilities and governance in 24 leading hospitals throughout Europe and Canada. We map the MDT practices of each hospital into a coherent framework, and identify KPIs and best practices by conducting semi-structured interviews with health care professionals in each hospital and performing workshops with the entire MDT team. The primary research focuses on five key categories: access to the MDT, processes, technology (such as EMR, data sharing tools, and artificial intelligence), culture and capabilities. Results: We see a high variation in the way MDTs are organized, in time for discussion per patient, in the model of care and current MDT practices across the 24 hospitals in this project, along our five identified categories. We developed an MDT maturity model where we score individual elements within these five categories to identify the hospitals with the best MDT practices, and a total of 23 key success factors along the MDT process. The maturity model and the key success factors can be used as a self-assessment tool for MDT-teams for continuous quality improvement. Conclusions: MDTs have the potential to be a standard part of the patient care pathway, where excellence is consistently achieved when discussing patient cases. This work has identified the practices that are done best in leading hospitals and that have the potential to be progressively adopted. Furthermore, we see that the potential of decision support tools and other artificial intelligence is rarely leveraged by MDTs.